@InProceedings{foland-martin:2017:Long,
  author    = {Foland, William  and  Martin, James H.},
  title     = {Abstract Meaning Representation Parsing using LSTM Recurrent Neural Networks},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {463--472},
  abstract  = {We present a system which parses sentences into Abstract Meaning
	Representations, improving state-of-the-art results for this task by more than
	5%.  AMR graphs represent semantic content using linguistic properties such
	as semantic roles, coreference, negation, and more.  The AMR parser does not
	rely on a syntactic pre-parse, or heavily engineered features, and uses five
	recurrent neural networks as the key architectural components for inferring AMR
	graphs.},
  url       = {http://aclweb.org/anthology/P17-1043}
}